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A tobit model with garch errors

Gabriele Fiorentini () and Giorgio Calzolari

Working Papers. Serie AD from Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie)

Abstract: In the context of time series regression, we extend the standard Tobitmodel to allow for the possibility of conditional heteroskedastic error processes of the GARCH type.We discuss the likelihood function of the Tobit model in the presence of conditionally heteroskedastic errors.Expressing the exact likelihood function turns out to be infeasible, and we propose anapproximation by treating the model as being conditionally Gaussian. The performance of theestimator is investigated by means of Monte Carlo simulations. We find that, when the errorterms follow a GARCH process, the proposed estimator considerably outperforms the standardTobit quasi maximum likelihood estimator. The efficency loss due to the approximationof the likelihood is finally evaluated.

Keywords: Censored regressions; conditional heteroskedasticity (search for similar items in EconPapers)
JEL-codes: C13 C15 C22 C24 (search for similar items in EconPapers)
Pages: 19 pages
Date: 1997-04
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Published by Ivie

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http://www.ivie.es/downloads/docs/wpasad/wpasad-1997-13.pdf Fisrt version / Primera version, 1997 (application/pdf)

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Journal Article: A tobit model with garch errors (1998) Downloads
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Persistent link: https://EconPapers.repec.org/RePEc:ivi:wpasad:1997-13

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